Neural Architecture Search (NAS) attempts to automate the design process of machine learning models with reinforcement learning [1]}, [2]}, evolutionary search [3]}, differentiable search [4]}, [5]}, and other methods [6]}, [7]}. Recent work in NAS has also reduced search costs [8]}, [9]}, [10]} and improved inference efficiency [11]}, [12]}, [13]}, [14]}, [15]}.
When designing fast models for inference with NAS, previous work employed multi-objective search [11]}, [17]}, [18]}, [19]}, [10]}, [21]}, [22]}, [23]}, [24]}, [25]} to consider accuracy together with performance/efficiency. However, their methods only passively use high level signals such as model size and latency.
